Multilayer Perceptrons

Multilayer perceptrons are feedforwardneural networks having at least
three layers of neurons, including an input layer, at least one hidden
layer and a single output layer. The activation functions of the input
layer is usually the identity function, while the activation functions
of the neurons in the hidden and output layers can be, in general, a mathematical
function which continuous first and second order derivatives. Example
of activation functions are identity, logistic sigmoid, hyperbolic tangent,
exponential and sine activation
functions.